{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\Carolina\OneDrive\Documentos\@ - UT Austin\Co-Authoring\Equation Balance - CW and PE\Replication Files - Final - Feb 2021\log_table_a2.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 6 Feb 2021, 15:47:53

{com}. do "C:\Users\Carolina\OneDrive\Documentos\@ - UT Austin\Co-Authoring\Equation Balance - CW and PE\Replication Files - Final - Feb 2021\Table A2 Replication Export.do"
{txt}
{com}. ***************************************************************************
. ***************************************************************************
. **Replication files for Enns, Moehlecke and Wlezien: "Detecting True 
. **Relationships in Time Series Data with Different Orders of Integration"**
. **Table A-2, appendix******************************************************
. **Last run: Feb 6th, 2021************************************************** 
. ***************************************************************************
. ***************************************************************************
. 
. 
. 
. ****************************************
. **Simulation Code to Replicate Table A2
. ****************************************
. 
. **********************************
. *Table A-2 layout
. **********************************
. *generate excel file to store Table 1 output
. putexcel set tablea2.xlsx, sheet(sheet1) replace
{res}{txt}Note: file will be replaced when the first {cmd:putexcel} command is issued

{com}. *add labels to table
. 
. putexcel A4 = "$\hat{c -(}\alpha{c )-}_1$"
{res}{txt}file tablea2.xlsx saved

{com}. putexcel A5 = "$\hat{c -(}\beta{c )-}_1$"
{res}{txt}file tablea2.xlsx saved

{com}. putexcel A6 = "$\hat{c -(}\beta{c )-}_2$"
{res}{txt}file tablea2.xlsx saved

{com}. 
. 
. putexcel B1 = "T = 50"
{res}{txt}file tablea2.xlsx saved

{com}. putexcel B2 = "$\rho = 0.2$"
{res}{txt}file tablea2.xlsx saved

{com}. putexcel B3 = "coef"
{res}{txt}file tablea2.xlsx saved

{com}. putexcel C3 = "%"
{res}{txt}file tablea2.xlsx saved

{com}. putexcel D2 = "$\rho = 0.5$"
{res}{txt}file tablea2.xlsx saved

{com}. putexcel D3 = "coef"
{res}{txt}file tablea2.xlsx saved

{com}. putexcel E3 = "%"
{res}{txt}file tablea2.xlsx saved

{com}. putexcel F2 = "$\rho = 0.8$"
{res}{txt}file tablea2.xlsx saved

{com}. putexcel F3 = "coef"
{res}{txt}file tablea2.xlsx saved

{com}. putexcel G3 = "%"
{res}{txt}file tablea2.xlsx saved

{com}. 
. putexcel H1 = "T = 100"
{res}{txt}file tablea2.xlsx saved

{com}. putexcel H2 = "$\rho = 0.2$"
{res}{txt}file tablea2.xlsx saved

{com}. putexcel H3 = "coef"
{res}{txt}file tablea2.xlsx saved

{com}. putexcel I3 = "%"
{res}{txt}file tablea2.xlsx saved

{com}. putexcel J2 = "$\rho = 0.5$"
{res}{txt}file tablea2.xlsx saved

{com}. putexcel J3 = "coef"
{res}{txt}file tablea2.xlsx saved

{com}. putexcel K3 = "%"
{res}{txt}file tablea2.xlsx saved

{com}. putexcel L2 = "$\rho = 0.8$"
{res}{txt}file tablea2.xlsx saved

{com}. putexcel L3 = "coef"
{res}{txt}file tablea2.xlsx saved

{com}. putexcel M3 = "%"
{res}{txt}file tablea2.xlsx saved

{com}. 
. putexcel N1 = "T = 200"
{res}{txt}file tablea2.xlsx saved

{com}. putexcel N2 = "$\rho = 0.2$"
{res}{txt}file tablea2.xlsx saved

{com}. putexcel N3 = "coef"
{res}{txt}file tablea2.xlsx saved

{com}. putexcel O3 = "%"
{res}{txt}file tablea2.xlsx saved

{com}. putexcel P2 = "$\rho = 0.5$"
{res}{txt}file tablea2.xlsx saved

{com}. putexcel P3 = "coef"
{res}{txt}file tablea2.xlsx saved

{com}. putexcel Q3 = "%"
{res}{txt}file tablea2.xlsx saved

{com}. putexcel R2 = "$\rho = 0.8$"
{res}{txt}file tablea2.xlsx saved

{com}. putexcel R3 = "coef"
{res}{txt}file tablea2.xlsx saved

{com}. putexcel S3 = "%"
{res}{txt}file tablea2.xlsx saved

{com}. 
. 
. 
. *************************
. **T=50; x1, rho=.2, .5, .8
. *************************
. set seed 457090451
{txt}
{com}. 
. ******
. *rho=.2
. *****
. *program drop combined_noq
. program define combined_noq, rclass
{txt}  1{com}. drop _all
{txt}  2{com}. set obs 50
{txt}  3{com}. gen t = _n
{txt}  4{com}. *gen stationary time series (x1)
. gen e1=invnorm(uniform())
{txt}  5{com}. gen x1=e1 if t==1
{txt}  6{com}. replace x1=.2*x1[_n-1] + e1 if t>1
{txt}  7{com}. *gen integrated time series (x2)
. gen u=invnorm(uniform())
{txt}  8{com}. gen x2=u if t==1
{txt}  9{com}. replace x2=x2[_n-1] + u if t>1
{txt} 10{com}. *gen combined times series (z) that his a function of x1 and x2
. gen q=invnorm(uniform())
{txt} 11{com}. gen z = x1 + x2
{txt} 12{com}. tsset t
{txt} 13{com}. reg z l.z x1 l.x1
{txt} 14{com}.         estat bgodfrey
{txt} 15{com}.         mat P = r(p)
{txt} 16{com}.         return scalar pvalue_bg = P[1,1] 
{txt} 17{com}. end
{txt}
{com}. 
. *Simulate the program "combined" N times and save the betas and standard errors.
. *Test whether an equation with mixed orders of integration (combined z, I(0) x1, I(1) x2)
. *can correctly identify TRUE relationships
. simulate pvalue_bg=r(pvalue_bg) _b _se, reps(1000) nodots: combined_noq
{p2colset 9 19 23 2}{...}

{txt}{p2col :command:}combined_noq{p_end}
{p2colset 1 19 23 2}{...}
{p2col :{txt}[{res:_eq2}]pvalue_bg:}{res:r(pvalue_bg)}{p_end}


{com}. 
. *Percent of simulations which a Breusch-Godfrey test rejects teh null of 
. *no serial correlation
. sum _eq2_pvalue_bg if _eq2_pvalue_bg<0.05

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
_eq2_pvalu~g {c |}{res}         66    .0260081    .0137048   .0008029   .0495008
{txt}
{com}. 
. *****************
. **coef
. *****************
. sum _sim_1 _b_x1 _sim_3

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}_sim_1 {c |}{res}      1,000    .8973016    .0866611   .4664565   1.062192
{txt}{space 7}_b_x1 {c |}{res}      1,000    1.000809      .14827   .6139219   1.450272
{txt}{space 6}_sim_3 {c |}{res}      1,000   -.8897199    .1753377   -1.42828   -.312953
{txt}
{com}. 
. *****************
. **%
. *****************
. 
. 
. *Generate t-statistic for each simulated regression and export results into excel
. 
. *export results from ADL rho = 0.2 to excel file
. **$\hat{c -(}\alpha{c )-}_1$
. *coef
. quietly sum _sim_1
{txt}
{com}. di %6.4f `r(mean)'
{res}0.8973
{txt}
{com}. putexcel B4 = `r(mean)', nformat("#0.####")
{res}{txt}file tablea2.xlsx saved

{com}. *%
. gen tstat_ly = abs(_sim_1/_sim_5) if abs(_sim_1/_sim_5)>=1.96
{txt}
{com}. quietly sum tstat_ly
{txt}
{com}. local perc = 100*`r(N)'/1000
{txt}
{com}. di `perc' 
{res}100
{txt}
{com}. putexcel C4 = `perc'
{res}{txt}file tablea2.xlsx saved

{com}. 
. **$\hat{c -(}\beta{c )-}_1$
. *coef
. quietly sum _b_x1
{txt}
{com}. di %6.4f `r(mean)'
{res}1.0008
{txt}
{com}. putexcel B5 = `r(mean)', nformat("#0.####")
{res}{txt}file tablea2.xlsx saved

{com}. *%
. gen tstat_x1 = abs(_b_x1/_se_x1) if abs(_b_x1/_se_x1) >=1.96
{txt}
{com}. quietly sum tstat_x1
{txt}
{com}. local perc = 100*`r(N)'/1000
{txt}
{com}. di `perc'
{res}100
{txt}
{com}. putexcel C5 = `perc'
{res}{txt}file tablea2.xlsx saved

{com}. 
. **$\hat{c -(}\beta{c )-}_2$
. *ceof
. quietly sum _sim_3
{txt}
{com}. di %6.4f `r(mean)'
{res}-0.8897
{txt}
{com}. putexcel B6 = `r(mean)', nformat("#0.####")
{res}{txt}file tablea2.xlsx saved

{com}. *%
. gen tstat_lx1 = abs(_sim_3/_sim_7) if abs(_sim_3/_sim_7)>=1.96
{txt}(2 missing values generated)

{com}. quietly sum tstat_lx1
{txt}
{com}. local perc = 100*`r(N)'/1000
{txt}
{com}. di `perc'
{res}99.8
{txt}
{com}. putexcel C6 = `perc'
{res}{txt}file tablea2.xlsx saved

{com}. 
. 
. ******
. *\rho=.5
. *****
. program drop combined_noq
{txt}
{com}. program define combined_noq, rclass
{txt}  1{com}. drop _all
{txt}  2{com}. set obs 50
{txt}  3{com}. gen t = _n
{txt}  4{com}. *gen stationary time series (x1)
. gen e1=invnorm(uniform())
{txt}  5{com}. gen x1=e1 if t==1
{txt}  6{com}. replace x1=.5*x1[_n-1] + e1 if t>1
{txt}  7{com}. *gen integrated time series (x2)
. gen u=invnorm(uniform())
{txt}  8{com}. gen x2=u if t==1
{txt}  9{com}. replace x2=x2[_n-1] + u if t>1
{txt} 10{com}. *gen combined times series (z) that his a function of x1 and x2
. gen q=invnorm(uniform())
{txt} 11{com}. gen z = x1 + x2
{txt} 12{com}. tsset t
{txt} 13{com}. reg z l.z x1 l.x1
{txt} 14{com}.         estat bgodfrey
{txt} 15{com}.         mat P = r(p)
{txt} 16{com}.         return scalar pvalue_bg = P[1,1] 
{txt} 17{com}. end
{txt}
{com}. 
. simulate pvalue_bg=r(pvalue_bg) _b _se, reps(1000) nodots: combined_noq
{p2colset 9 19 23 2}{...}

{txt}{p2col :command:}combined_noq{p_end}
{p2colset 1 19 23 2}{...}
{p2col :{txt}[{res:_eq2}]pvalue_bg:}{res:r(pvalue_bg)}{p_end}


{com}. 
. *Percent of simulations which a Breusch-Godfrey test rejects teh null of 
. *no serial correlation
. sum _eq2_pvalue_bg if _eq2_pvalue_bg<0.05

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
_eq2_pvalu~g {c |}{res}         69    .0252573    .0150117    .001115   .0498105
{txt}
{com}. 
. *****************
. **coef
. *****************
. sum _sim_1 _b_x1 _sim_3

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}_sim_1 {c |}{res}      1,000    .8943916    .0871468    .385197   1.055132
{txt}{space 7}_b_x1 {c |}{res}      1,000    .9980619    .1540015   .4833065   1.680423
{txt}{space 6}_sim_3 {c |}{res}      1,000   -.8902309    .1747751  -1.531473  -.1169714
{txt}
{com}. 
. *****************
. **%
. *****************
. 
. *Generate t-statistic for each simulated regression and export results into excel
. *export results from ADL rho = 0.5 to excel file
. **$\hat{c -(}\alpha{c )-}_1$
. *coef
. quietly sum _sim_1
{txt}
{com}. di %6.4f `r(mean)'
{res}0.8944
{txt}
{com}. putexcel D4 = `r(mean)', nformat("#0.####")
{res}{txt}file tablea2.xlsx saved

{com}. *%
. gen tstat_ly = abs(_sim_1/_sim_5) if abs(_sim_1/_sim_5)>=1.96
{txt}
{com}. quietly sum tstat_ly
{txt}
{com}. local perc = 100*`r(N)'/1000
{txt}
{com}. di `perc' 
{res}100
{txt}
{com}. putexcel E4 = `perc'
{res}{txt}file tablea2.xlsx saved

{com}. 
. **$\hat{c -(}\beta{c )-}_1$
. *coef
. quietly sum _b_x1
{txt}
{com}. di %6.4f `r(mean)'
{res}0.9981
{txt}
{com}. putexcel D5 = `r(mean)', nformat("#0.####")
{res}{txt}file tablea2.xlsx saved

{com}. *%
. gen tstat_x1 = abs(_b_x1/_se_x1) if abs(_b_x1/_se_x1) >=1.96
{txt}
{com}. quietly sum tstat_x1
{txt}
{com}. local perc = 100*`r(N)'/1000
{txt}
{com}. di `perc'
{res}100
{txt}
{com}. putexcel E5 = `perc'
{res}{txt}file tablea2.xlsx saved

{com}. 
. **$\hat{c -(}\beta{c )-}_2$
. *coef
. quietly sum _sim_3
{txt}
{com}. di %6.4f `r(mean)'
{res}-0.8902
{txt}
{com}. putexcel D6 = `r(mean)', nformat("#0.####")
{res}{txt}file tablea2.xlsx saved

{com}. *%
. gen tstat_lx1 = abs(_sim_3/_sim_7) if abs(_sim_3/_sim_7)>=1.96
{txt}(2 missing values generated)

{com}. quietly sum tstat_lx1
{txt}
{com}. local perc = 100*`r(N)'/1000
{txt}
{com}. di `perc'
{res}99.8
{txt}
{com}. putexcel E6 = `perc'
{res}{txt}file tablea2.xlsx saved

{com}. 
. ******
. *rho=.8
. *****
. program drop combined_noq
{txt}
{com}. program define combined_noq, rclass
{txt}  1{com}. drop _all
{txt}  2{com}. set obs 50
{txt}  3{com}. gen t = _n
{txt}  4{com}. *gen stationary time series (x1)
. gen e1=invnorm(uniform())
{txt}  5{com}. gen x1=e1 if t==1
{txt}  6{com}. replace x1=.8*x1[_n-1] + e1 if t>1
{txt}  7{com}. *gen integrated time series (x2)
. gen u=invnorm(uniform())
{txt}  8{com}. gen x2=u if t==1
{txt}  9{com}. replace x2=x2[_n-1] + u if t>1
{txt} 10{com}. *gen combined times series (z) that his a function of x1 and x2
. gen q=invnorm(uniform())
{txt} 11{com}. gen z = x1 + x2
{txt} 12{com}. tsset t
{txt} 13{com}. reg z l.z x1 l.x1
{txt} 14{com}.         estat bgodfrey
{txt} 15{com}.         mat P = r(p)
{txt} 16{com}.         return scalar pvalue_bg = P[1,1] 
{txt} 17{com}. end
{txt}
{com}. 
. simulate pvalue_bg=r(pvalue_bg) _b _se, reps(1000) nodots: combined_noq
{p2colset 9 19 23 2}{...}

{txt}{p2col :command:}combined_noq{p_end}
{p2colset 1 19 23 2}{...}
{p2col :{txt}[{res:_eq2}]pvalue_bg:}{res:r(pvalue_bg)}{p_end}


{com}. 
. *Percent of simulations which a Breusch-Godfrey test rejects teh null of 
. *no serial correlation
. sum _eq2_pvalue_bg if _eq2_pvalue_bg<0.05

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
_eq2_pvalu~g {c |}{res}         56    .0237459    .0124711    .002343   .0451359
{txt}
{com}. 
. *****************
. **coef
. *****************
. sum _sim_1 _b_x1 _sim_3

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}_sim_1 {c |}{res}      1,000    .8831313    .0955864   .4407059   1.067998
{txt}{space 7}_b_x1 {c |}{res}      1,000    1.005026    .1523373   .4628813   1.481538
{txt}{space 6}_sim_3 {c |}{res}      1,000   -.8893831    .1758803  -1.435487  -.2428044
{txt}
{com}. 
. *****************
. **%
. *****************
. 
. *Generate t-statistic for each simulated regression and export results into excel
. *export results from ADL rho = 0.8 to excel file
. **$\hat{c -(}\alpha{c )-}_1$
. *coef
. quietly sum _sim_1
{txt}
{com}. di %6.4f `r(mean)'
{res}0.8831
{txt}
{com}. putexcel F4 = `r(mean)', nformat("#0.####")
{res}{txt}file tablea2.xlsx saved

{com}. *%
. gen tstat_ly = abs(_sim_1/_sim_5) if abs(_sim_1/_sim_5)>=1.96
{txt}
{com}. quietly sum tstat_ly
{txt}
{com}. local perc = 100*`r(N)'/1000
{txt}
{com}. di `perc' 
{res}100
{txt}
{com}. putexcel G4 = `perc'
{res}{txt}file tablea2.xlsx saved

{com}. 
. **$\hat{c -(}\beta{c )-}_1$
. *coef
. quietly sum _b_x1
{txt}
{com}. di %6.4f `r(mean)'
{res}1.0050
{txt}
{com}. putexcel F5 = `r(mean)', nformat("#0.####")
{res}{txt}file tablea2.xlsx saved

{com}. *%
. gen tstat_x1 = abs(_b_x1/_se_x1) if abs(_b_x1/_se_x1) >=1.96
{txt}
{com}. quietly sum tstat_x1
{txt}
{com}. local perc = 100*`r(N)'/1000
{txt}
{com}. di `perc'
{res}100
{txt}
{com}. putexcel G5 = `perc'
{res}{txt}file tablea2.xlsx saved

{com}. 
. **$\hat{c -(}\beta{c )-}_2$
. *coef
. quietly sum _sim_3
{txt}
{com}. di %6.4f `r(mean)'
{res}-0.8894
{txt}
{com}. putexcel F6 = `r(mean)', nformat("#0.####")
{res}{txt}file tablea2.xlsx saved

{com}. *%
. gen tstat_lx1 = abs(_sim_3/_sim_7) if abs(_sim_3/_sim_7)>=1.96
{txt}(2 missing values generated)

{com}. quietly sum tstat_lx1
{txt}
{com}. local perc = 100*`r(N)'/1000
{txt}
{com}. di `perc'
{res}99.8
{txt}
{com}. putexcel G6 = `perc'
{res}{txt}file tablea2.xlsx saved

{com}. 
. 
. *************************
. **T=100; x1, rho=.2, .5, .8
. *************************
. 
. ******
. *rho=.2
. *****
. program drop combined_noq
{txt}
{com}. program define combined_noq, rclass
{txt}  1{com}. drop _all
{txt}  2{com}. set obs 100
{txt}  3{com}. gen t = _n
{txt}  4{com}. *gen stationary time series (x1)
. gen e1=invnorm(uniform())
{txt}  5{com}. gen x1=e1 if t==1
{txt}  6{com}. replace x1=.2*x1[_n-1] + e1 if t>1
{txt}  7{com}. *gen integrated time series (x2)
. gen u=invnorm(uniform())
{txt}  8{com}. gen x2=u if t==1
{txt}  9{com}. replace x2=x2[_n-1] + u if t>1
{txt} 10{com}. *gen combined times series (z) that his a function of x1 and x2
. gen q=invnorm(uniform())
{txt} 11{com}. gen z = x1 + x2
{txt} 12{com}. tsset t
{txt} 13{com}. reg z l.z x1 l.x1
{txt} 14{com}.         estat bgodfrey
{txt} 15{com}.         mat P = r(p)
{txt} 16{com}.         return scalar pvalue_bg = P[1,1] 
{txt} 17{com}. end
{txt}
{com}. 
. *Simulate the program "combined" N times and save the betas and standard errors.
. *Test whether an equation with mixed orders of integration (combined z, I(0) x1, I(1) x2)
. *can correctly identify TRUE relationships
. simulate pvalue_bg=r(pvalue_bg) _b _se, reps(1000) nodots: combined_noq
{p2colset 9 19 23 2}{...}

{txt}{p2col :command:}combined_noq{p_end}
{p2colset 1 19 23 2}{...}
{p2col :{txt}[{res:_eq2}]pvalue_bg:}{res:r(pvalue_bg)}{p_end}


{com}. 
. 
. *Percent of simulations which a Breusch-Godfrey test rejects teh null of 
. *no serial correlation
. sum _eq2_pvalue_bg if _eq2_pvalue_bg<0.05

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
_eq2_pvalu~g {c |}{res}         50    .0250565     .015388   .0009977   .0499748
{txt}
{com}. 
. *****************
. **coef
. *****************
. sum _sim_1 _b_x1 _sim_3

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}_sim_1 {c |}{res}      1,000    .9473731    .0427902   .7032871   1.021705
{txt}{space 7}_b_x1 {c |}{res}      1,000     1.00214    .1008273   .6934514    1.36182
{txt}{space 6}_sim_3 {c |}{res}      1,000   -.9509378    .1112476  -1.322374  -.6533844
{txt}
{com}. 
. *****************
. **%
. *****************
. 
. *Generate t-statistic for each simulated regression and export results into excel
. *export results from ADL rho = 0.2 to excel file
. **$\hat{c -(}\alpha{c )-}_1$
. *coef
. quietly sum _sim_1
{txt}
{com}. di %6.4f `r(mean)'
{res}0.9474
{txt}
{com}. putexcel H4 = `r(mean)', nformat("#0.####")
{res}{txt}file tablea2.xlsx saved

{com}. *%
. gen tstat_ly = abs(_sim_1/_sim_5) if abs(_sim_1/_sim_5)>=1.96
{txt}
{com}. quietly sum tstat_ly
{txt}
{com}. local perc = 100*`r(N)'/1000
{txt}
{com}. di `perc' 
{res}100
{txt}
{com}. putexcel I4 = `perc'
{res}{txt}file tablea2.xlsx saved

{com}. 
. **$\hat{c -(}\beta{c )-}_1$
. *coef
. quietly sum _b_x1
{txt}
{com}. di %6.4f `r(mean)'
{res}1.0021
{txt}
{com}. putexcel H5 = `r(mean)', nformat("#0.####")
{res}{txt}file tablea2.xlsx saved

{com}. *%
. gen tstat_x1 = abs(_b_x1/_se_x1) if abs(_b_x1/_se_x1) >=1.96
{txt}
{com}. quietly sum tstat_x1
{txt}
{com}. local perc = 100*`r(N)'/1000
{txt}
{com}. di `perc'
{res}100
{txt}
{com}. putexcel I5 = `perc'
{res}{txt}file tablea2.xlsx saved

{com}. 
. **$\hat{c -(}\beta{c )-}_2$
. *coef
. quietly sum _sim_3
{txt}
{com}. di %6.4f `r(mean)'
{res}-0.9509
{txt}
{com}. putexcel H6 = `r(mean)', nformat("#0.####")
{res}{txt}file tablea2.xlsx saved

{com}. *%
. gen tstat_lx1 = abs(_sim_3/_sim_7) if abs(_sim_3/_sim_7)>=1.96
{txt}
{com}. quietly sum tstat_lx1
{txt}
{com}. local perc = 100*`r(N)'/1000
{txt}
{com}. di `perc'
{res}100
{txt}
{com}. putexcel I6 = `perc'
{res}{txt}file tablea2.xlsx saved

{com}. 
. 
. ******
. *rho=.5
. *****
. program drop combined_noq
{txt}
{com}. program define combined_noq, rclass
{txt}  1{com}. drop _all
{txt}  2{com}. set obs 100
{txt}  3{com}. gen t = _n
{txt}  4{com}. *gen stationary time series (x1)
. gen e1=invnorm(uniform())
{txt}  5{com}. gen x1=e1 if t==1
{txt}  6{com}. replace x1=.5*x1[_n-1] + e1 if t>1
{txt}  7{com}. *gen integrated time series (x2)
. gen u=invnorm(uniform())
{txt}  8{com}. gen x2=u if t==1
{txt}  9{com}. replace x2=x2[_n-1] + u if t>1
{txt} 10{com}. *gen combined times series (z) that his a function of x1 and x2
. gen q=invnorm(uniform())
{txt} 11{com}. gen z = x1 + x2
{txt} 12{com}. tsset t
{txt} 13{com}. reg z l.z x1 l.x1
{txt} 14{com}.         estat bgodfrey
{txt} 15{com}.         mat P = r(p)
{txt} 16{com}.         return scalar pvalue_bg = P[1,1] 
{txt} 17{com}. end
{txt}
{com}. 
. simulate pvalue_bg=r(pvalue_bg) _b _se, reps(1000) nodots: combined_noq
{p2colset 9 19 23 2}{...}

{txt}{p2col :command:}combined_noq{p_end}
{p2colset 1 19 23 2}{...}
{p2col :{txt}[{res:_eq2}]pvalue_bg:}{res:r(pvalue_bg)}{p_end}


{com}. 
. *Percent of simulations which a Breusch-Godfrey test rejects teh null of 
. *no serial correlation
. sum _eq2_pvalue_bg if _eq2_pvalue_bg<0.05

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
_eq2_pvalu~g {c |}{res}         48    .0254188    .0143197   .0036748   .0494369
{txt}
{com}. 
. *****************
. **coef
. *****************
. sum _sim_1 _b_x1 _sim_3

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}_sim_1 {c |}{res}      1,000    .9470095    .0449899   .5879751   1.021872
{txt}{space 7}_b_x1 {c |}{res}      1,000    .9989002     .099268   .5895661   1.337767
{txt}{space 6}_sim_3 {c |}{res}      1,000   -.9442184    .1098522  -1.338031  -.5501938
{txt}
{com}. 
. *****************
. **%
. *****************
. 
. *Generate t-statistic for each simulated regression and export results into excel
. *export results from ADL rho = 0.5 to excel file
. **$\hat{c -(}\alpha{c )-}_1$
. *coef
. quietly sum _sim_1
{txt}
{com}. di %6.4f `r(mean)'
{res}0.9470
{txt}
{com}. putexcel J4 = `r(mean)', nformat("#0.####")
{res}{txt}file tablea2.xlsx saved

{com}. *%
. gen tstat_ly = abs(_sim_1/_sim_5) if abs(_sim_1/_sim_5)>=1.96
{txt}
{com}. quietly sum tstat_ly
{txt}
{com}. local perc = 100*`r(N)'/1000
{txt}
{com}. di `perc' 
{res}100
{txt}
{com}. putexcel K4 = `perc'
{res}{txt}file tablea2.xlsx saved

{com}. 
. **$\hat{c -(}\beta{c )-}_1$
. *coef
. quietly sum _b_x1
{txt}
{com}. di %6.4f `r(mean)'
{res}0.9989
{txt}
{com}. putexcel J5 = `r(mean)', nformat("#0.####")
{res}{txt}file tablea2.xlsx saved

{com}. *%
. gen tstat_x1 = abs(_b_x1/_se_x1) if abs(_b_x1/_se_x1) >=1.96
{txt}
{com}. quietly sum tstat_x1
{txt}
{com}. local perc = 100*`r(N)'/1000
{txt}
{com}. di `perc'
{res}100
{txt}
{com}. putexcel K5 = `perc'
{res}{txt}file tablea2.xlsx saved

{com}. 
. **$\hat{c -(}\beta{c )-}_2$
. *coef
. quietly sum _sim_3
{txt}
{com}. di %6.4f `r(mean)'
{res}-0.9442
{txt}
{com}. putexcel J6 = `r(mean)', nformat("#0.####")
{res}{txt}file tablea2.xlsx saved

{com}. *%
. gen tstat_lx1 = abs(_sim_3/_sim_7) if abs(_sim_3/_sim_7)>=1.96
{txt}
{com}. quietly sum tstat_lx1
{txt}
{com}. local perc = 100*`r(N)'/1000
{txt}
{com}. di `perc'
{res}100
{txt}
{com}. putexcel K6 = `perc'
{res}{txt}file tablea2.xlsx saved

{com}. 
. 
. ******
. *rho=.8
. *****
. program drop combined_noq
{txt}
{com}. program define combined_noq, rclass
{txt}  1{com}. drop _all
{txt}  2{com}. set obs 100
{txt}  3{com}. gen t = _n
{txt}  4{com}. *gen stationary time series (x1)
. gen e1=invnorm(uniform())
{txt}  5{com}. gen x1=e1 if t==1
{txt}  6{com}. replace x1=.8*x1[_n-1] + e1 if t>1
{txt}  7{com}. *gen integrated time series (x2)
. gen u=invnorm(uniform())
{txt}  8{com}. gen x2=u if t==1
{txt}  9{com}. replace x2=x2[_n-1] + u if t>1
{txt} 10{com}. *gen combined times series (z) that his a function of x1 and x2
. gen q=invnorm(uniform())
{txt} 11{com}. gen z = x1 + x2
{txt} 12{com}. tsset t
{txt} 13{com}. reg z l.z x1 l.x1
{txt} 14{com}.         estat bgodfrey
{txt} 15{com}.         mat P = r(p)
{txt} 16{com}.         return scalar pvalue_bg = P[1,1] 
{txt} 17{com}. end
{txt}
{com}. 
. simulate pvalue_bg=r(pvalue_bg) _b _se, reps(1000) nodots: combined_noq
{p2colset 9 19 23 2}{...}

{txt}{p2col :command:}combined_noq{p_end}
{p2colset 1 19 23 2}{...}
{p2col :{txt}[{res:_eq2}]pvalue_bg:}{res:r(pvalue_bg)}{p_end}


{com}. 
. *Percent of simulations which a Breusch-Godfrey test rejects teh null of 
. *no serial correlation
. sum _eq2_pvalue_bg if _eq2_pvalue_bg<0.05

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
_eq2_pvalu~g {c |}{res}         39    .0300367    .0135012   .0018633   .0495501
{txt}
{com}. 
. *****************
. **coef
. *****************
. sum _sim_1 _b_x1 _sim_3

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}_sim_1 {c |}{res}      1,000    .9434806    .0455987   .6823872   1.035939
{txt}{space 7}_b_x1 {c |}{res}      1,000    .9948179    .0992398   .6565605   1.383911
{txt}{space 6}_sim_3 {c |}{res}      1,000   -.9388281    .1123986  -1.262789  -.5781761
{txt}
{com}. 
. *****************
. **%
. *****************
. 
. *Generate t-statistic for each simulated regression and export results into excel
. *export results from ADL rho = 0.8 to excel file
. 
. **$\hat{c -(}\alpha{c )-}_1$
. *coef
. quietly sum _sim_1
{txt}
{com}. di %6.4f `r(mean)'
{res}0.9435
{txt}
{com}. putexcel L4 = `r(mean)', nformat("#0.####")
{res}{txt}file tablea2.xlsx saved

{com}. *%
. gen tstat_ly = abs(_sim_1/_sim_5) if abs(_sim_1/_sim_5)>=1.96
{txt}
{com}. quietly sum tstat_ly
{txt}
{com}. local perc = 100*`r(N)'/1000
{txt}
{com}. di `perc' 
{res}100
{txt}
{com}. putexcel M4 = `perc'
{res}{txt}file tablea2.xlsx saved

{com}. 
. **$\hat{c -(}\beta{c )-}_1$
. *coef
. quietly sum _b_x1
{txt}
{com}. di %6.4f `r(mean)'
{res}0.9948
{txt}
{com}. putexcel L5 = `r(mean)', nformat("#0.####")
{res}{txt}file tablea2.xlsx saved

{com}. *%
. gen tstat_x1 = abs(_b_x1/_se_x1) if abs(_b_x1/_se_x1) >=1.96
{txt}
{com}. quietly sum tstat_x1
{txt}
{com}. local perc = 100*`r(N)'/1000
{txt}
{com}. di `perc'
{res}100
{txt}
{com}. putexcel M5 = `perc'
{res}{txt}file tablea2.xlsx saved

{com}. 
. **$\hat{c -(}\beta{c )-}_2$
. *coef
. quietly sum _sim_3
{txt}
{com}. di %6.4f `r(mean)'
{res}-0.9388
{txt}
{com}. putexcel L6 = `r(mean)', nformat("#0.####")
{res}{txt}file tablea2.xlsx saved

{com}. *%
. gen tstat_lx1 = abs(_sim_3/_sim_7) if abs(_sim_3/_sim_7)>=1.96
{txt}
{com}. quietly sum tstat_lx1
{txt}
{com}. local perc = 100*`r(N)'/1000
{txt}
{com}. di `perc'
{res}100
{txt}
{com}. putexcel M6 = `perc'
{res}{txt}file tablea2.xlsx saved

{com}. 
. 
. 
. *************************
. **T=200; x1, rho=.2, .5, .8
. *************************
. 
. ******
. *rho=.2
. *****
. program drop combined_noq
{txt}
{com}. program define combined_noq, rclass
{txt}  1{com}. drop _all
{txt}  2{com}. set obs 200
{txt}  3{com}. gen t = _n
{txt}  4{com}. *gen stationary time series (x1)
. gen e1=invnorm(uniform())
{txt}  5{com}. gen x1=e1 if t==1
{txt}  6{com}. replace x1=.2*x1[_n-1] + e1 if t>1
{txt}  7{com}. *gen integrated time series (x2)
. gen u=invnorm(uniform())
{txt}  8{com}. gen x2=u if t==1
{txt}  9{com}. replace x2=x2[_n-1] + u if t>1
{txt} 10{com}. *gen combined times series (z) that his a function of x1 and x2
. gen q=invnorm(uniform())
{txt} 11{com}. gen z = x1 + x2
{txt} 12{com}. tsset t
{txt} 13{com}. reg z l.z x1 l.x1
{txt} 14{com}.         estat bgodfrey
{txt} 15{com}.         mat P = r(p)
{txt} 16{com}.         return scalar pvalue_bg = P[1,1] 
{txt} 17{com}. end
{txt}
{com}. 
. *Simulate the program "combined" N times and save the betas and standard errors.
. *Test whether an equation with mixed orders of integration (combined z, I(0) x1, I(1) x2)
. *can correctly identify TRUE relationships
. simulate pvalue_bg=r(pvalue_bg) _b _se, reps(1000) nodots: combined_noq
{p2colset 9 19 23 2}{...}

{txt}{p2col :command:}combined_noq{p_end}
{p2colset 1 19 23 2}{...}
{p2col :{txt}[{res:_eq2}]pvalue_bg:}{res:r(pvalue_bg)}{p_end}


{com}. 
. *Percent of simulations which a Breusch-Godfrey test rejects teh null of 
. *no serial correlation
. sum _eq2_pvalue_bg if _eq2_pvalue_bg<0.05

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
_eq2_pvalu~g {c |}{res}         52    .0256915    .0148038   .0002442    .049167
{txt}
{com}. 
. *****************
. **coef
. *****************
. sum _sim_1 _b_x1 _sim_3

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}_sim_1 {c |}{res}      1,000    .9733459    .0211187   .8412822   1.012155
{txt}{space 7}_b_x1 {c |}{res}      1,000     .999756    .0744138   .7678134   1.245534
{txt}{space 6}_sim_3 {c |}{res}      1,000   -.9748862    .0738789  -1.226624  -.7591182
{txt}
{com}. 
. *****************
. **%
. *****************
. *Generate t-statistic for each simulated regression and export results into excel
. *export results from ADL rho = 0.2 to excel file
. 
. **$\hat{c -(}\alpha{c )-}_1$
. *coef
. quietly sum _sim_1
{txt}
{com}. di %6.4f `r(mean)'
{res}0.9733
{txt}
{com}. putexcel N4 = `r(mean)', nformat("#0.####")
{res}{txt}file tablea2.xlsx saved

{com}. *%
. gen tstat_ly = abs(_sim_1/_sim_5) if abs(_sim_1/_sim_5)>=1.96
{txt}
{com}. quietly sum tstat_ly
{txt}
{com}. local perc = 100*`r(N)'/1000
{txt}
{com}. di `perc' 
{res}100
{txt}
{com}. putexcel O4 = `perc'
{res}{txt}file tablea2.xlsx saved

{com}. 
. **$\hat{c -(}\beta{c )-}_1$
. *coef
. quietly sum _b_x1
{txt}
{com}. di %6.4f `r(mean)'
{res}0.9998
{txt}
{com}. putexcel N5 = `r(mean)', nformat("#0.####")
{res}{txt}file tablea2.xlsx saved

{com}. *%
. gen tstat_x1 = abs(_b_x1/_se_x1) if abs(_b_x1/_se_x1) >=1.96
{txt}
{com}. quietly sum tstat_x1
{txt}
{com}. local perc = 100*`r(N)'/1000
{txt}
{com}. di `perc'
{res}100
{txt}
{com}. putexcel O5 = `perc'
{res}{txt}file tablea2.xlsx saved

{com}. 
. **$\hat{c -(}\beta{c )-}_2$
. *coef
. quietly sum _sim_3
{txt}
{com}. di %6.4f `r(mean)'
{res}-0.9749
{txt}
{com}. putexcel N6 = `r(mean)', nformat("#0.####")
{res}{txt}file tablea2.xlsx saved

{com}. *%
. gen tstat_lx1 = abs(_sim_3/_sim_7) if abs(_sim_3/_sim_7)>=1.96
{txt}
{com}. quietly sum tstat_lx1
{txt}
{com}. local perc = 100*`r(N)'/1000
{txt}
{com}. di `perc'
{res}100
{txt}
{com}. putexcel O6 = `perc'
{res}{txt}file tablea2.xlsx saved

{com}. 
. 
. 
. ******
. *rho=.5
. *****
. program drop combined_noq
{txt}
{com}. program define combined_noq, rclass
{txt}  1{com}. drop _all
{txt}  2{com}. set obs 200
{txt}  3{com}. gen t = _n
{txt}  4{com}. *gen stationary time series (x1)
. gen e1=invnorm(uniform())
{txt}  5{com}. gen x1=e1 if t==1
{txt}  6{com}. replace x1=.5*x1[_n-1] + e1 if t>1
{txt}  7{com}. *gen integrated time series (x2)
. gen u=invnorm(uniform())
{txt}  8{com}. gen x2=u if t==1
{txt}  9{com}. replace x2=x2[_n-1] + u if t>1
{txt} 10{com}. *gen combined times series (z) that his a function of x1 and x2
. gen q=invnorm(uniform())
{txt} 11{com}. gen z = x1 + x2
{txt} 12{com}. tsset t
{txt} 13{com}. reg z l.z x1 l.x1
{txt} 14{com}.         estat bgodfrey
{txt} 15{com}.         mat P = r(p)
{txt} 16{com}.         return scalar pvalue_bg = P[1,1] 
{txt} 17{com}. end
{txt}
{com}. 
. simulate pvalue_bg=r(pvalue_bg) _b _se, reps(1000) nodots: combined_noq
{p2colset 9 19 23 2}{...}

{txt}{p2col :command:}combined_noq{p_end}
{p2colset 1 19 23 2}{...}
{p2col :{txt}[{res:_eq2}]pvalue_bg:}{res:r(pvalue_bg)}{p_end}


{com}. 
. *Percent of simulations which a Breusch-Godfrey test rejects teh null of 
. *no serial correlation
. sum _eq2_pvalue_bg if _eq2_pvalue_bg<0.05

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
_eq2_pvalu~g {c |}{res}         65    .0267372    .0147021   .0021206   .0499883
{txt}
{com}. 
. *****************
. **coef
. *****************
. sum _sim_1 _b_x1 _sim_3

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}_sim_1 {c |}{res}      1,000    .9735395    .0221826   .8440306   1.014134
{txt}{space 7}_b_x1 {c |}{res}      1,000    .9965964    .0716561   .7733069   1.228376
{txt}{space 6}_sim_3 {c |}{res}      1,000   -.9725902    .0749435  -1.198368  -.7327971
{txt}
{com}. 
. *****************
. **%
. *****************
. *Generate t-statistic for each simulated regression and export results into excel
. *export results from ADL rho = 0.2 to excel file
. 
. **$\hat{c -(}\alpha{c )-}_1$
. *coef
. quietly sum _sim_1
{txt}
{com}. di %6.4f `r(mean)'
{res}0.9735
{txt}
{com}. putexcel P4 = `r(mean)', nformat("#0.####")
{res}{txt}file tablea2.xlsx saved

{com}. *%
. gen tstat_ly = abs(_sim_1/_sim_5) if abs(_sim_1/_sim_5)>=1.96
{txt}
{com}. quietly sum tstat_ly
{txt}
{com}. local perc = 100*`r(N)'/1000
{txt}
{com}. di `perc' 
{res}100
{txt}
{com}. putexcel Q4 = `perc'
{res}{txt}file tablea2.xlsx saved

{com}. 
. **$\hat{c -(}\beta{c )-}_1$
. *coef
. quietly sum _b_x1
{txt}
{com}. di %6.4f `r(mean)'
{res}0.9966
{txt}
{com}. putexcel P5 = `r(mean)', nformat("#0.####")
{res}{txt}file tablea2.xlsx saved

{com}. *%
. gen tstat_x1 = abs(_b_x1/_se_x1) if abs(_b_x1/_se_x1) >=1.96
{txt}
{com}. quietly sum tstat_x1
{txt}
{com}. local perc = 100*`r(N)'/1000
{txt}
{com}. di `perc'
{res}100
{txt}
{com}. putexcel Q5 = `perc'
{res}{txt}file tablea2.xlsx saved

{com}. 
. **$\hat{c -(}\beta{c )-}_2$
. *coef
. quietly sum _sim_3
{txt}
{com}. di %6.4f `r(mean)'
{res}-0.9726
{txt}
{com}. putexcel P6 = `r(mean)', nformat("#0.####")
{res}{txt}file tablea2.xlsx saved

{com}. *%
. gen tstat_lx1 = abs(_sim_3/_sim_7) if abs(_sim_3/_sim_7)>=1.96
{txt}
{com}. quietly sum tstat_lx1
{txt}
{com}. local perc = 100*`r(N)'/1000
{txt}
{com}. di `perc'
{res}100
{txt}
{com}. putexcel Q6 = `perc'
{res}{txt}file tablea2.xlsx saved

{com}. 
. 
. 
. ******
. *rho=.8
. *****
. program drop combined_noq
{txt}
{com}. program define combined_noq, rclass
{txt}  1{com}. drop _all
{txt}  2{com}. set obs 200
{txt}  3{com}. gen t = _n
{txt}  4{com}. *gen stationary time series (x1)
. gen e1=invnorm(uniform())
{txt}  5{com}. gen x1=e1 if t==1
{txt}  6{com}. replace x1=.8*x1[_n-1] + e1 if t>1
{txt}  7{com}. *gen integrated time series (x2)
. gen u=invnorm(uniform())
{txt}  8{com}. gen x2=u if t==1
{txt}  9{com}. replace x2=x2[_n-1] + u if t>1
{txt} 10{com}. *gen combined times series (z) that his a function of x1 and x2
. gen q=invnorm(uniform())
{txt} 11{com}. gen z = x1 + x2
{txt} 12{com}. tsset t
{txt} 13{com}. reg z l.z x1 l.x1
{txt} 14{com}.         estat bgodfrey
{txt} 15{com}.         mat P = r(p)
{txt} 16{com}.         return scalar pvalue_bg = P[1,1] 
{txt} 17{com}. end
{txt}
{com}. 
. simulate pvalue_bg=r(pvalue_bg) _b _se, reps(2000) nodots: combined_noq
{p2colset 9 19 23 2}{...}

{txt}{p2col :command:}combined_noq{p_end}
{p2colset 1 19 23 2}{...}
{p2col :{txt}[{res:_eq2}]pvalue_bg:}{res:r(pvalue_bg)}{p_end}


{com}. 
. *Percent of simulations which a Breusch-Godfrey test rejects teh null of 
. *no serial correlation
. sum _eq2_pvalue_bg if _eq2_pvalue_bg<0.05

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
_eq2_pvalu~g {c |}{res}        120    .0272762    .0143884   .0000192   .0499299
{txt}
{com}. 
. *****************
. **coef
. *****************
. sum _sim_1 _b_x1 _sim_3

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}_sim_1 {c |}{res}      2,000    .9728002     .023825   .8349644   1.024172
{txt}{space 7}_b_x1 {c |}{res}      2,000    1.000768    .0727373   .7517576    1.24224
{txt}{space 6}_sim_3 {c |}{res}      2,000     -.97546    .0783011  -1.219743   -.693004
{txt}
{com}. 
. *****************
. ** %
. *****************
. *Generate t-statistic for each simulated regression and export results into excel
. *export results from ADL rho = 0.2 to excel file
. 
. **$\hat{c -(}\alpha{c )-}_1$
. *coef
. quietly sum _sim_1
{txt}
{com}. di %6.4f `r(mean)'
{res}0.9728
{txt}
{com}. putexcel R4 = `r(mean)', nformat("#0.####")
{res}{txt}file tablea2.xlsx saved

{com}. *%
. gen tstat_ly = abs(_sim_1/_sim_5) if abs(_sim_1/_sim_5)>=1.96
{txt}
{com}. quietly sum tstat_ly
{txt}
{com}. local perc = 100*`r(N)'/2000
{txt}
{com}. di `perc' 
{res}100
{txt}
{com}. putexcel S4 = `perc'
{res}{txt}file tablea2.xlsx saved

{com}. 
. **$\hat{c -(}\beta{c )-}_1$
. *coef
. quietly sum _b_x1
{txt}
{com}. di %6.4f `r(mean)'
{res}1.0008
{txt}
{com}. putexcel R5 = `r(mean)', nformat("#0.####")
{res}{txt}file tablea2.xlsx saved

{com}. *%
. gen tstat_x1 = abs(_b_x1/_se_x1) if abs(_b_x1/_se_x1) >=1.96
{txt}
{com}. quietly sum tstat_x1
{txt}
{com}. local perc = 100*`r(N)'/2000
{txt}
{com}. di `perc'
{res}100
{txt}
{com}. putexcel S5 = `perc'
{res}{txt}file tablea2.xlsx saved

{com}. 
. **$\hat{c -(}\beta{c )-}_2$
. *coef
. quietly sum _sim_3
{txt}
{com}. di %6.4f `r(mean)'
{res}-0.9755
{txt}
{com}. putexcel R6 = `r(mean)', nformat("#0.####")
{res}{txt}file tablea2.xlsx saved

{com}. *%
. gen tstat_lx1 = abs(_sim_3/_sim_7) if abs(_sim_3/_sim_7)>=1.96
{txt}
{com}. quietly sum tstat_lx1
{txt}
{com}. local perc = 100*`r(N)'/2000
{txt}
{com}. di `perc'
{res}100
{txt}
{com}. putexcel S6 = `perc'
{res}{txt}file tablea2.xlsx saved

{com}. 
. 
{txt}end of do-file

{com}. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\Carolina\OneDrive\Documentos\@ - UT Austin\Co-Authoring\Equation Balance - CW and PE\Replication Files - Final - Feb 2021\log_table_a2.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res} 6 Feb 2021, 15:53:10
{txt}{.-}
{smcl}
{txt}{sf}{ul off}